Abstract: The disclosure describes systems and methods for a stabilization mechanism. The stabilization mechanism may be used in conjunction with an imaging device. The method may be performed by a control system of the stabilization mechanism and includes obtaining a device setting from an imaging device. The method may also include obtaining a configuration of the stabilization mechanism. The method includes determining a soft stop based on the device setting, the configuration, or both. The soft stop may be a virtual hard stop that indicates to the stabilization mechanism to reduce speed as a field of view of the imaging device approaches the soft stop. The method may also include setting an image stabilization mechanism parameter based on the determined soft stop.

Abstract: This disclosure relates to systems and methods for controlling an unmanned aerial vehicle. Boundaries of a user-defined space may be obtained. The boundaries of the user-defined space may be fixed with respect to some reference frame. A user-defined operation associated with the user-selected space may be obtained. Position of the unmanned aerial vehicle may be tracked during an unmanned aerial flight. Responsive to the unmanned aerial vehicle entering the user-defined space, the unmanned aerial vehicle may be automatically controlled to perform the user-defined operation.

Abstract: A GNSS antenna system for receiving GNSS signals in the L1 and L2/L5 frequency band, and to an unmanned aerial vehicle (UAV) comprising the GNSS antenna system.

Abstract: A system for providing 3D surveying of an environment by an autonomous robotic vehicle comprising a SLAM unit for carrying out a simultaneous localization and mapping process, a path planning unit to determine a path to be taken by the autonomous robotic vehicle, and a lidar device. The lidar device is configured to generate the lidar data which allows the SLAM unit to receive the lidar data as part of the perception data for the SLAM process. The path planning unit is configured to determine the path to be taken by carrying out an evaluation of a further trajectory within a map of the environment in relation to an estimated point distribution map for an estimated 3D point cloud, which is provided by the lidar device on the further trajectory and projected onto the map of the environment.

Abstract: A system for 3D surveying of an environment by an unmanned ground vehicle (UGV) and an unmanned aerial vehicle (UAV) has two lidar devices. A reference unit has a first and a second marker in a spatially fixed arrangement. An automatic detection of the first marker is carried out for a coordinative measurement by the first lidar device to determine relative position data for providing relative position information of the first marker with respect to the first lidar device. The relative position data and spatial 3D information is used for an automatic detection and a coordinative measurement of the second marker by the second lidar device. The coordinative measurements are used for a referencing of lidar data of the UGV lidar device and lidar data of the UAV lidar device with respect to a common coordinate system.

Abstract: Systems and methods are disclosed for image signal processing. For example, methods may include, based on a sequence of orientation estimates for an image sensor and an orientation setpoint, invoking a mechanical stabilization system to adjust an orientation of the image sensor toward the orientation setpoint; receiving an image from the image sensor; determining an orientation error between the orientation of the image sensor and the orientation setpoint during capture of the image; and, based on the orientation error, invoking an electronic image stabilization module to correct the image for a rotation corresponding to the orientation error to obtain a stabilized image.

Abstract: Controlling an unmanned aerial vehicle to traverse a portion of an operational environment of the unmanned aerial vehicle may include obtaining an object detection type, obtaining object detection input data, obtaining relative object orientation data based on the object detection type and the object detection input data, and performing an object avoidance operation based on the relative object orientation data. The object detection type may be monocular object detection, which may include obtaining the relative object orientation data by obtaining motion data indicating a change of spatial location for the unmanned aerial vehicle between obtaining the first image and obtaining the second image based on searching along epipolar lines to obtain optical flow data.

Abstract: This disclosure relates to systems and methods for controlling an unmanned aerial vehicle. Boundaries of a user-defined space may be obtained. The boundaries of the user-defined space may be fixed with respect to some reference frame. A user-defined operation associated with the user-selected space may be obtained. Position of the unmanned aerial vehicle may be tracked during an unmanned aerial flight. Responsive to the unmanned aerial vehicle entering the user-defined space, the unmanned aerial vehicle may be automatically controlled to perform the user-defined operation.

Abstract: Systems and methods are disclosed for image signal processing. For example, methods may include determining an orientation setpoint for an image sensor; based on a sequence of orientation estimates for the image sensor and the orientation setpoint, invoking a mechanical stabilization system to adjust an orientation of the image sensor toward the orientation setpoint; receiving an image from the image sensor; determining an orientation error between the orientation of the image sensor and the orientation setpoint during capture of the image; based on the orientation error, invoking an electronic image stabilization module to correct the image for a rotation corresponding to the orientation error to obtain a stabilized image; and storing, displaying, or transmitting an output image based on the stabilized image.

Abstract: This disclosure relates to systems and methods for vehicle guidance. Stereo images may be obtained at different times using a stereo image sensor. A depth image may be determined based on an earlier obtained pair of stereo images. The depth image may be refined based on predictions of an earlier stereo image and a later obtained stereo image. Depth information for an environment around a vehicle may be obtained. The depth information may characterize distances between the vehicle and the environment around the vehicle. A spherical depth map may be generated from the depth information. Maneuver controls for the vehicle may be provided based on the spherical depth map.

Abstract: The invention relates to a computer implemented method for identifying transparent and/or mirroring plane candidates from measurement data resulting from scanning an environment by a laser scanner emitting distance measuring light pulses.

Abstract: Controlling an unmanned aerial vehicle to traverse a portion of an operational environment of the unmanned aerial vehicle may include obtaining an object detection type, obtaining object detection input data, obtaining relative object orientation data based on the object detection type and the object detection input data, and performing a collision avoidance operation based on the relative object orientation data. The object detection type may be monocular object detection, which may include obtaining the relative object orientation data by obtaining motion data indicating a change of spatial location for the unmanned aerial vehicle between obtaining the first image and obtaining the second image based on searching along epipolar lines to obtain optical flow data.

Abstract: This disclosure relates to systems and methods for vehicle guidance. Stereo images may be obtained at different times using a stereo image sensor. A depth image may be determined based on an earlier obtained pair of stereo images. The depth image may be refined based on predictions of an earlier stereo image and a later obtained stereo image. Depth information for an environment around a vehicle may be obtained. The depth information may characterize distances between the vehicle and the environment around the vehicle. A spherical depth map may be generated from the depth information. Maneuver controls for the vehicle may be provided based on the spherical depth map.

Abstract: Stabilizing an image capture device includes stabilizing the image capture device as the image capture device captures images; responsive to detecting an exceptional activity, stopping the stabilizing of the image capture device; and after the exceptional activity is completed, stabilizing the image capture device again. An image stabilization device for stabilizing an imaging device includes a processor that is configured to set at least one of a pitch angle or a roll angle of the image stabilization device to respective constant values and allow a yaw angle of the image stabilization device to vary; while the yaw angle is less than a threshold angle, maintain the yaw angle at a constant relative to a reference platform; when the yaw angle reaches the threshold angle, stop keeping the yaw angle relative to a reference platform constant; and set the yaw angle to follow a direction of motion of the reference platform.

Abstract: The disclosure describes systems and methods for a stabilization mechanism. The stabilization mechanism may be used in conjunction with an imaging device. The method may be performed by a control system of the stabilization mechanism and includes obtaining a device setting from an imaging device. The method may also include obtaining a configuration of the stabilization mechanism. The method includes determining a soft stop based on the device setting, the configuration, or both. The soft stop may be a virtual hard stop that indicates to the stabilization mechanism to reduce speed as a field of view of the imaging device approaches the soft stop. The method may also include setting an image stabilization mechanism parameter based on the determined soft stop.

Abstract: The disclosure describes systems and methods for calibrating an image stabilization mechanism. One method includes a control system sending a command to thermally condition one or more sensors to a predetermined temperature. During thermal conditioning to the predetermined temperature, the control system sends a command to drive one or more motors of the image stabilization mechanism to cause movement of an imaging device coupled to the image stabilization mechanism. After thermal conditioning to the predetermined temperature, the control system sends a command to stop driving the one or more motors of the image stabilization mechanism to stop movement of the imaging device coupled to the image stabilization mechanism. After stopping the driving of the one or more motors, the control system sends a command to calibrate the one or more sensors.

Abstract: This disclosure relates to systems and methods for determining predicted risk for a flight path of an unmanned aerial vehicle. A previously stored three-dimensional representation of a user-selected location may be obtained. The three-dimensional representation may be derived from depth maps of the user-selected location generated during previous unmanned aerial vehicle flights. The three-dimensional representation may reflect a presence of objects and object existence accuracies for the individual objects. The object existence accuracies for the individual objects may provide information about accuracy of existence of the individual objects within the user-selected location. A user-created flight path may be obtained for a future unmanned aerial flight within the three-dimensional representation of the user-selected location. Predicted risk may be determined for individual portions of the user-created flight path based upon the three-dimensional representation of the user-selected location.

Abstract: The invention relates to a computer implemented UAV control method comprising, in a target selection mode, the steps of displaying on a touch sensitive display a 3D-view of an environment of a UAV; Overlaying a moveable target indicating symbol to the 3D-view of the environment, wherein the target indicating symbol is moveable in the 3D-view by a touch input; While moving the target indicating symbol, continuously determining a location of the target indicating symbol in the 3D-view and dynamically changing the appearance of the target indicating symbol such that it creates the impression of being displayed in an orientation matching the orientation of a face over which the target indicating symbol is located wherein the orientation of the respective face is derived from stored 3D-data or from the 3D-view; And selecting a target based on the location of the target indicating symbol.

Abstract: Controlling an unmanned aerial vehicle may include obtaining a first image from a fixed orientation image capture device of the unmanned aerial vehicle, obtaining a second image from an adjustable orientation image capture device of the unmanned aerial vehicle, obtaining feature correlation data based on the first image and the second image, obtaining relative image capture device orientation calibration data based on the feature correlation data, the relative image capture device orientation calibration data indicating an orientation of the adjustable orientation image capture device relative to the fixed orientation image capture device, obtaining relative object orientation data based on the relative image capture device orientation calibration data, the relative object orientation data representing a three-dimensional orientation of an external object relative to the adjustable orientation image capture device, and controlling a trajectory of the unmanned aerial vehicle in response to the relative object

Abstract: This disclosure relates to systems and methods for determining predicted risk for a flight path of an unmanned aerial vehicle. A previously stored three-dimensional representation of a user-selected location may be obtained. The three-dimensional representation may be derived from depth maps of the user-selected location generated during previous unmanned aerial vehicle flights. The three-dimensional representation may reflect a presence of objects and object existence accuracies for the individual objects. The object existence accuracies for the individual objects may provide information about accuracy of existence of the individual objects within the user-selected location. A user-created flight path may be obtained for a future unmanned aerial flight within the three-dimensional representation of the user-selected location. Predicted risk may be determined for individual portions of the user-created flight path based upon the three-dimensional representation of the user-selected location.